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将饮食评估与青少年生活方式相结合。

Merging dietary assessment with the adolescent lifestyle.

作者信息

Schap T E, Zhu F, Delp E J, Boushey C J

机构信息

Department of Nutrition Science, College of Health and Human Sciences, Purdue University, West Lafayette, IN, USA.

出版信息

J Hum Nutr Diet. 2014 Jan;27 Suppl 1(0 1):82-8. doi: 10.1111/jhn.12071. Epub 2013 Mar 13.

Abstract

The use of image-based dietary assessment methods shows promise for improving dietary self-report among children. The Technology Assisted Dietary Assessment (TADA) food record application is a self-administered food record specifically designed to address the burden and human error associated with conventional methods of dietary assessment. Users would take images of foods and beverages at all eating occasions using a mobile telephone or mobile device with an integrated camera [e.g. Apple iPhone, Apple iPod Touch (Apple Inc., Cupertino, CA, USA); Nexus One (Google, Mountain View, CA, USA)]. Once the images are taken, the images are transferred to a back-end server for automated analysis. The first step in this process is image analysis (i.e. segmentation, feature extraction and classification), which allows for automated food identification. Portion size estimation is also automated via segmentation and geometric shape template modeling. The results of the automated food identification and volume estimation can be indexed with the Food and Nutrient Database for Dietary Studies to provide a detailed diet analysis for use in epidemiological or intervention studies. Data collected during controlled feeding studies in a camp-like setting have allowed for formative evaluation and validation of the TADA food record application. This review summarises the system design and the evidence-based development of image-based methods for dietary assessment among children.

摘要

使用基于图像的膳食评估方法有望改善儿童的膳食自我报告。技术辅助膳食评估(TADA)食物记录应用程序是一种自我管理的食物记录,专门设计用于解决与传统膳食评估方法相关的负担和人为误差。用户在所有用餐场合使用配备集成摄像头的移动电话或移动设备(如苹果iPhone、苹果iPod Touch(美国加利福尼亚州库比蒂诺苹果公司);Nexus One(美国加利福尼亚州山景城谷歌公司))拍摄食物和饮料的图像。图像拍摄完成后,会传输到后端服务器进行自动分析。这个过程的第一步是图像分析(即分割、特征提取和分类),这使得食物能够自动识别。份量估计也通过分割和几何形状模板建模实现自动化。自动食物识别和体积估计的结果可以与膳食研究的食物和营养数据库进行索引,以提供详细的饮食分析,用于流行病学或干预研究。在类似营地的环境中进行的对照喂养研究期间收集的数据,使得对TADA食物记录应用程序进行了形成性评估和验证。本综述总结了用于儿童膳食评估的基于图像方法的系统设计和循证开发。

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本文引用的文献

1
FOOD TEXTURE DESCRIPTORS BASED ON FRACTAL AND LOCAL GRADIENT INFORMATION.
Proc Eur Signal Process Conf EUSIPCO. 2011 Aug-Sep;2011:764-768. Epub 2015 Apr 2.
2
INTEGRATED DATABASE SYSTEM FOR MOBILE DIETARY ASSESSMENT AND ANALYSIS.
Proc (IEEE Int Conf Multimed Expo). 2011 Jul;2011. doi: 10.1109/ICME.2011.6012202. Epub 2011 Sep 6.
3
COMBINING GLOBAL AND LOCAL FEATURES FOR FOOD IDENTIFICATION IN DIETARY ASSESSMENT.
Proc Int Conf Image Proc. 2011 Sep;2011:1789-1792. doi: 10.1109/ICIP.2011.6115809. Epub 2011 Dec 29.
4
Segmentation Assisted Food Classification for Dietary Assessment.
Proc SPIE Int Soc Opt Eng. 2011 Jan 24;7873:78730B. doi: 10.1117/12.877036.
5
Technology-Assisted Dietary Assessment.
Proc SPIE Int Soc Opt Eng. 2008 Mar 20;6814:681411. doi: 10.1117/12.778616.
6
Multilevel Segmentation for Food Classification in Dietary Assessment.
Proc Int Symp Image Signal Process Anal. 2011 Sep 4:337-342.
7
Volume Estimation Using Food Specific Shape Templates in Mobile Image-Based Dietary Assessment.
Proc SPIE Int Soc Opt Eng. 2011 Feb 7;7873:78730K. doi: 10.1117/12.876669.
8
AN IMAGE ANALYSIS SYSTEM FOR DIETARY ASSESSMENT AND EVALUATION.
Proc Int Conf Image Proc. 2010:1853-1856. doi: 10.1109/ICIP.2010.5650848.
9
Personal Dietary Assessment Using Mobile Devices.
Proc SPIE Int Soc Opt Eng. 2009 Jan 1;7246. doi: 10.1117/12.813556.

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